{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "intense-ecology",
   "metadata": {},
   "source": [
    "# Neural Network Classifier & Regressor\n",
    "\n",
    "In this tutorial we show how the `NeuralNetworkClassifier` and `NeuralNetworkRegressor` are used.\n",
    "Both take as an input a (Quantum) `NeuralNetwork` and leverage it in a specific context.\n",
    "In both cases we also provide a pre-configured variant for convenience, the Variational Quantum Classifier (`VQC`) and Variational Quantum Regressor (`VQR`). The tutorial is structured as follows:\n",
    "\n",
    "\n",
    "1. [Classification](#Classification) \n",
    "    * Classification with an `EstimatorQNN`\n",
    "    * Classification with a `SamplerQNN`\n",
    "    * Variational Quantum Classifier (`VQC`)\n",
    "    \n",
    "    \n",
    "2. [Regression](#Regression)\n",
    "    * Regression with an `EstimatorQNN`\n",
    "    * Variational Quantum Regressor (`VQR`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "functioning-sword",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit import Parameter\n",
    "from qiskit.circuit.library import real_amplitudes, zz_feature_map\n",
    "from qiskit_machine_learning.optimizers import COBYLA, L_BFGS_B\n",
    "from qiskit_machine_learning.utils import algorithm_globals\n",
    "\n",
    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier, VQC\n",
    "from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor, VQR\n",
    "from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\n",
    "from qiskit_machine_learning.circuit.library import qnn_circuit\n",
    "\n",
    "algorithm_globals.random_seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compact-divide",
   "metadata": {},
   "source": [
    "## Classification\n",
    "\n",
    "We prepare a simple classification dataset to illustrate the following algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "short-pierre",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "num_samples = 20\n",
    "X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1\n",
    "y01 = 1 * (np.sum(X, axis=1) >= 0)  # in { 0,  1}\n",
    "y = 2 * y01 - 1  # in {-1, +1}\n",
    "y_one_hot = np.zeros((num_samples, 2))\n",
    "for i in range(num_samples):\n",
    "    y_one_hot[i, y01[i]] = 1\n",
    "\n",
    "for x, y_target in zip(X, y):\n",
    "    if y_target == 1:\n",
    "        plt.plot(x[0], x[1], \"bo\")\n",
    "    else:\n",
    "        plt.plot(x[0], x[1], \"go\")\n",
    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "religious-history",
   "metadata": {},
   "source": [
    "### Classification with an `EstimatorQNN`\n",
    "\n",
    "First we show how an `EstimatorQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `EstimatorQNN` is expected to return one-dimensional output in $[-1, +1]$. This only works for binary classification and we assign the two classes to $\\{-1, +1\\}$. To simplify the composition of parameterized quantum circuit from a feature map and an ansatz we can use the `qnn_circuit`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ceed90df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1875.91x200.667 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct QNN with the qnn_circuit's default zz_feature_map feature map and real_amplitudes ansatz.\n",
    "qc, fm_params, anz_params = qnn_circuit(num_qubits=2)\n",
    "qc.draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "formed-animal",
   "metadata": {},
   "source": [
    "Create a quantum neural network.  As we are performing a local statevector simulation, we will set the `estimator` parameter from `qiskit.primitives.StatevectorEstimator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "determined-hands",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Estimator requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "from qiskit.primitives import StatevectorEstimator as Estimator\n",
    "\n",
    "estimator = Estimator()\n",
    "estimator_qnn = EstimatorQNN(\n",
    "    circuit=qc, input_params=fm_params, weight_params=anz_params, estimator=estimator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acute-casting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24998863]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QNN maps inputs to [-1, +1]\n",
    "estimator_qnn.forward(X[0, :], algorithm_globals.random.random(estimator_qnn.num_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stone-holiday",
   "metadata": {},
   "source": [
    "We will add a callback function called `callback_graph`. This will be called for each iteration of the optimizer and will be passed two parameters: the current weights and the value of the objective function at those weights. For our function, we append the value of the objective function to an array so we can plot iteration versus objective function value and update the graph with each iteration. However, you can do whatever you want with a callback function as long as it gets the two parameters mentioned passed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "similar-controversy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# callback function that draws a live plot when the .fit() method is called\n",
    "def callback_graph(weights, obj_func_eval):\n",
    "    clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "lesser-receiver",
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct neural network classifier\n",
    "estimator_classifier = NeuralNetworkClassifier(\n",
    "    estimator_qnn, optimizer=COBYLA(maxiter=60), callback=callback_graph\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adopted-editor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create empty array for callback to store evaluations of the objective function\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# fit classifier to data\n",
    "estimator_classifier.fit(X, y)\n",
    "\n",
    "# return to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# score classifier\n",
    "estimator_classifier.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "civilian-analysis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate data points\n",
    "y_predict = estimator_classifier.predict(X)\n",
    "\n",
    "# plot results\n",
    "# red == wrongly classified\n",
    "for x, y_target, y_p in zip(X, y, y_predict):\n",
    "    if y_target == 1:\n",
    "        plt.plot(x[0], x[1], \"bo\")\n",
    "    else:\n",
    "        plt.plot(x[0], x[1], \"go\")\n",
    "    if y_target != y_p:\n",
    "        plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n",
    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "japanese-seattle",
   "metadata": {},
   "source": [
    "Now, when the model is trained, we can explore the weights of the neural network. Please note, the number of weights is defined by ansatz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "offshore-basket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.61473052, -0.81218415,  0.3983311 , -0.18685675,  1.22468766,\n",
       "        0.97744221,  1.85731519, -1.2402504 ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator_classifier.weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "determined-standing",
   "metadata": {},
   "source": [
    "### Classification with a `SamplerQNN`\n",
    "\n",
    "Next we show how a `SamplerQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `SamplerQNN` is expected to return $d$-dimensional probability vector as output, where $d$ denotes the number of classes. \n",
    "The underlying `Sampler` primitive returns quasi-distributions of bit strings and we just need to define a mapping from the measured bitstrings to the different classes. For binary classification we use the parity mapping. Again we can use the `QNNCircuit` class to set up a parameterized quantum circuit from a feature map and ansatz of our choice. Please note that, once again, we are setting the `StatevectorSampler` instance from `qiskit.primitives` to the QNN and relying on `statevector`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1ff56f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1541.46x200.667 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct a quantum circuit from the default zz_feature_map feature map and a customized real_amplitudes ansatz\n",
    "qc, fm_params, anz_params = qnn_circuit(ansatz=real_amplitudes(num_inputs, reps=1))\n",
    "qc.draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "young-sensitivity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parity maps bitstrings to 0 or 1\n",
    "def parity(x):\n",
    "    return \"{:b}\".format(x).count(\"1\") % 2\n",
    "\n",
    "\n",
    "output_shape = 2  # corresponds to the number of classes, possible outcomes of the (parity) mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "statutory-mercury",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "from qiskit.primitives import StatevectorSampler as Sampler\n",
    "\n",
    "sampler = Sampler()\n",
    "# construct QNN\n",
    "sampler_qnn = SamplerQNN(\n",
    "    circuit=qc,\n",
    "    input_params=fm_params,\n",
    "    weight_params=anz_params,\n",
    "    interpret=parity,\n",
    "    output_shape=output_shape,\n",
    "    sampler=sampler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "hybrid-orlando",
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct classifier\n",
    "sampler_classifier = NeuralNetworkClassifier(\n",
    "    neural_network=sampler_qnn, optimizer=COBYLA(maxiter=30), callback=callback_graph\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "adult-newman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.55"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create empty array for callback to store evaluations of the objective function\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# fit classifier to data\n",
    "sampler_classifier.fit(X, y01)\n",
    "\n",
    "# return to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# score classifier\n",
    "sampler_classifier.score(X, y01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "angry-bulgarian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate data points\n",
    "y_predict = sampler_classifier.predict(X)\n",
    "\n",
    "# plot results\n",
    "# red == wrongly classified\n",
    "for x, y_target, y_p in zip(X, y01, y_predict):\n",
    "    if y_target == 1:\n",
    "        plt.plot(x[0], x[1], \"bo\")\n",
    "    else:\n",
    "        plt.plot(x[0], x[1], \"go\")\n",
    "    if y_target != y_p:\n",
    "        plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n",
    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assisted-individual",
   "metadata": {},
   "source": [
    "Again, once the model is trained we can take a look at the weights. As we set `reps=1` explicitly in our ansatz, we can see less parameters than in the previous model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "indonesian-bulletin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.2391    ,  1.09296819, -0.33722594, -0.23161501])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler_classifier.weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "champion-approval",
   "metadata": {},
   "source": [
    "### Variational Quantum Classifier (`VQC`)\n",
    "\n",
    "The `VQC` is a special variant of the `NeuralNetworkClassifier` with a `SamplerQNN`. It applies a parity mapping (or extensions to multiple classes) to map from the bitstring to the classification, which results in a probability vector, which is interpreted as a one-hot encoded result. By default, it applies this the `CrossEntropyLoss` function that expects labels given in one-hot encoded format and will return predictions in that format too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "legislative-dublin",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "# construct feature map, ansatz, and optimizer\n",
    "feature_map = zz_feature_map(num_inputs)\n",
    "ansatz = real_amplitudes(num_inputs, reps=1)\n",
    "\n",
    "# construct variational quantum classifier\n",
    "vqc = VQC(\n",
    "    feature_map=feature_map,\n",
    "    ansatz=ansatz,\n",
    "    loss=\"cross_entropy\",\n",
    "    optimizer=COBYLA(maxiter=30),\n",
    "    callback=callback_graph,\n",
    "    sampler=sampler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "geographic-adjustment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create empty array for callback to store evaluations of the objective function\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# fit classifier to data\n",
    "vqc.fit(X, y_one_hot)\n",
    "\n",
    "# return to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# score classifier\n",
    "vqc.score(X, y_one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "stopped-heavy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate data points\n",
    "y_predict = vqc.predict(X)\n",
    "\n",
    "# plot results\n",
    "# red == wrongly classified\n",
    "for x, y_target, y_p in zip(X, y_one_hot, y_predict):\n",
    "    if y_target[0] == 1:\n",
    "        plt.plot(x[0], x[1], \"bo\")\n",
    "    else:\n",
    "        plt.plot(x[0], x[1], \"go\")\n",
    "    if not np.all(y_target == y_p):\n",
    "        plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n",
    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grave-testament",
   "metadata": {},
   "source": [
    "### Multiple classes with VQC\n",
    "In this section we generate an artificial dataset that contains samples of three classes and show how to train a model to classify this dataset. This example shows how to tackle more interesting problems in machine learning. Of course, for a sake of short training time we prepare a tiny dataset. We employ `make_classification` from SciKit-Learn to generate a dataset. There 10 samples in the dataset, 2 features, that means we can still have a nice plot of the dataset, as well as no redundant features, these are features are generated as a combinations of the other features. Also, we have 3 different classes in the dataset, each classes one kind of centroid and we set class separation to `2.0`, a slight increase from the default value of `1.0` to ease the classification problem.\n",
    "\n",
    "Once the dataset is generated we scale the features into the range `[0, 1]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "plastic-dividend",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X, y = make_classification(\n",
    "    n_samples=10,\n",
    "    n_features=2,\n",
    "    n_classes=3,\n",
    "    n_redundant=0,\n",
    "    n_clusters_per_class=1,\n",
    "    class_sep=2.0,\n",
    "    random_state=algorithm_globals.random_seed,\n",
    ")\n",
    "X = MinMaxScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forced-disclosure",
   "metadata": {},
   "source": [
    "Let's see how our dataset looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "premier-drill",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x130d3dd9df0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deadly-response",
   "metadata": {},
   "source": [
    "We also transform labels and make them categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "exposed-bailey",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n"
     ]
    }
   ],
   "source": [
    "y_cat = np.empty(y.shape, dtype=str)\n",
    "y_cat[y == 0] = \"A\"\n",
    "y_cat[y == 1] = \"B\"\n",
    "y_cat[y == 2] = \"C\"\n",
    "print(y_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instructional-headquarters",
   "metadata": {},
   "source": [
    "We create an instance of `VQC` similar to the previous example, but in this case we pass a minimal set of parameters. Instead of feature map and ansatz we pass just the number of qubits that is equal to the number of features in the dataset, an optimizer with a low number of iteration to reduce training time, a quantum instance, and a callback to observe progress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "latin-result",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "vqc = VQC(\n",
    "    num_qubits=2,\n",
    "    optimizer=COBYLA(maxiter=30),\n",
    "    callback=callback_graph,\n",
    "    sampler=sampler,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proper-bookmark",
   "metadata": {},
   "source": [
    "Start the training process in the same way as in previous examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "reported-pioneer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create empty array for callback to store evaluations of the objective function\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# fit classifier to data\n",
    "vqc.fit(X, y_cat)\n",
    "\n",
    "# return to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# score classifier\n",
    "vqc.score(X, y_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-renaissance",
   "metadata": {},
   "source": [
    "Despite we had the low number of iterations, we achieved quite a good score. Let see the output of the `predict` method and compare the output with the ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "employed-patient",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted labels: ['A' 'A' 'B' 'A' 'A' 'A' 'B' 'B' 'A' 'B']\n",
      "Ground truth:     ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n"
     ]
    }
   ],
   "source": [
    "predict = vqc.predict(X)\n",
    "print(f\"Predicted labels: {predict}\")\n",
    "print(f\"Ground truth:     {y_cat}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "guided-secret",
   "metadata": {},
   "source": [
    "## Regression\n",
    "\n",
    "We prepare a simple regression dataset to illustrate the following algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "iraqi-flavor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_samples = 20\n",
    "eps = 0.2\n",
    "lb, ub = -np.pi, np.pi\n",
    "X_ = np.linspace(lb, ub, num=50).reshape(50, 1)\n",
    "f = lambda x: np.sin(x)\n",
    "\n",
    "X = (ub - lb) * algorithm_globals.random.random([num_samples, 1]) + lb\n",
    "y = f(X[:, 0]) + eps * (2 * algorithm_globals.random.random(num_samples) - 1)\n",
    "\n",
    "plt.plot(X_, f(X_), \"r--\")\n",
    "plt.plot(X, y, \"bo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "talented-capitol",
   "metadata": {},
   "source": [
    "### Regression with an `EstimatorQNN`\n",
    "\n",
    "Here we restrict to regression with an `EstimatorQNN` that returns values in $[-1, +1]$. More complex and also multi-dimensional models could be constructed, also based on `SamplerQNN` but that exceeds the scope of this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "perfect-kelly",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Estimator requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "# construct simple feature map\n",
    "param_x = Parameter(\"x\")\n",
    "feature_map = QuantumCircuit(1, name=\"fm\")\n",
    "feature_map.ry(param_x, 0)\n",
    "\n",
    "# construct simple ansatz\n",
    "param_y = Parameter(\"y\")\n",
    "ansatz = QuantumCircuit(1, name=\"vf\")\n",
    "ansatz.ry(param_y, 0)\n",
    "\n",
    "# construct a circuit\n",
    "qc, fm_params, anz_params = qnn_circuit(feature_map=feature_map, ansatz=ansatz)\n",
    "\n",
    "# construct QNN\n",
    "regression_estimator_qnn = EstimatorQNN(\n",
    "    circuit=qc, input_params=fm_params, weight_params=anz_params, estimator=estimator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "velvet-marks",
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct the regressor from the neural network\n",
    "regressor = NeuralNetworkRegressor(\n",
    "    neural_network=regression_estimator_qnn,\n",
    "    loss=\"squared_error\",\n",
    "    optimizer=L_BFGS_B(maxiter=5),\n",
    "    callback=callback_graph,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "working-mongolia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9735416717503363"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create empty array for callback to store evaluations of the objective function\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# fit to data\n",
    "regressor.fit(X, y)\n",
    "\n",
    "# return to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# score the result\n",
    "regressor.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "diverse-conservative",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot target function\n",
    "plt.plot(X_, f(X_), \"r--\")\n",
    "\n",
    "# plot data\n",
    "plt.plot(X, y, \"bo\")\n",
    "\n",
    "# plot fitted line\n",
    "y_ = regressor.predict(X_)\n",
    "plt.plot(X_, y_, \"g-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "false-india",
   "metadata": {},
   "source": [
    "Similarly to the classification models, we can obtain an array of trained weights by querying a corresponding property of the model. In this model we have only one parameter defined as `param_y` above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "terminal-turner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.53376712])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offensive-legislation",
   "metadata": {},
   "source": [
    "### Regression with the Variational Quantum Regressor (`VQR`)\n",
    "\n",
    "Similar to the `VQC` for classification, the `VQR` is a special variant of the `NeuralNetworkRegressor` with a `EstimatorQNN`. By default it considers the `L2Loss` function to minimize the mean squared error between predictions and targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "offensive-entry",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Estimator requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "vqr = VQR(\n",
    "    feature_map=feature_map,\n",
    "    ansatz=ansatz,\n",
    "    optimizer=L_BFGS_B(maxiter=5),\n",
    "    callback=callback_graph,\n",
    "    estimator=estimator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cooperative-helmet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9781336848314651"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create empty array for callback to store evaluations of the objective function\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# fit regressor\n",
    "vqr.fit(X, y)\n",
    "\n",
    "# return to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# score result\n",
    "vqr.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "genetic-cambridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot target function\n",
    "plt.plot(X_, f(X_), \"r--\")\n",
    "\n",
    "# plot data\n",
    "plt.plot(X, y, \"bo\")\n",
    "\n",
    "# plot fitted line\n",
    "y_ = vqr.predict(X_)\n",
    "plt.plot(X_, y_, \"g-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "backed-visit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.4.3</td></tr><tr><td><code>qiskit_machine_learning</code></td><td>0.9.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.12.9</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td colspan='2'>Thu Jul 17 16:45:26 2025 Eastern Daylight Time</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tutorial_magics\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83cbe3e7-6349-4cbb-a933-ad3bdbc0a8a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bfdd90f-ad64-423f-9146-3e3e41a24094",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
